What is the Google Data Analytics course at Coursera all about? Learn how to process and analyze data, use key analysis tools, apply R programming, and create visualizations that can inform key business decisions. Data analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making. It is ETL. This program includes over 180 hours of instruction and hundreds of practice-based assessments, which will help you simulate real-world data analytics scenarios that are critical for success in the workplace. You’ll need to successfully finish the projects to earn your Certificate. Skills you’ll gain will include: Data cleaning, problem solving, critical thinking, data ethics, and data visualization. Platforms and tools you will learn include: Presentations, Spreadsheets, SQL, Tableau and R Programming. It takes about 3 months to complete at 20 hours a week, or 6 months at 10 hours a week. That’s about 260 hours to complete. There are 8 courses designed to be taken in order. There is now an Advanced Data Analytics course at Coursera.
The certificate program was launched in March 2021. The Google Data Analytics certificate has become the most popular Professional Certificate on Coursera globally! There are people on LinkedIn that are talking about this certficate. Go to People around the world taking the Google Data Analytics Professional Certificate
As per the webpage, just a few key points on the 8 courses…
1. Foundations: Data, Data, Everywhere
overview. Learn about key analytical skills (data cleaning, data analysis, data visualization, understanding context, having a technical mindset, data design, and data strategy) and tools (spreadsheets, SQL, R programming, and Tableau). terms and concepts. data life cycle and the data analysis process.
2. Ask Questions to Make Data-Driven Decisions
gain an understanding of data-driven decision-making and how data analysts present findings. communicate with others, and spreadsheets. structured thinking and how they can help analysts better understand problems and develop solutions. managing the expectations of stakeholders; dashboards, Tableau; Asking SMART and effective questions;
3. Prepare Data for Exploration
spreadsheets and SQL. BigQuery. decide which data to collect for analysis. where’s the data? do we have internal data? do we need external data (like demographics or economic data) structured and unstructured data, data types, and data formats. data ethics and data privacy; metadata; how to access databases and extract, filter, and sort the data they contain. Learn the best practices for organizing data and keeping it secure, and report on cleaning results; clean out extra spaces/characters; inconsistent spellings (Ontario, Ont, ON); inconsistent capitalization; removing duplicates; replacing blanks with null.
4. Process Data from Dirty to Clean
how to check and clean your data using spreadsheets and SQL. Apply basic SQL functions for cleaning and transforming data. errors; how to verify the results of cleaning data; Remove outliers; Explore the elements and importance of data cleaning reports. Statistics, hypothesis testing, and margin of error.
5. Analyze Data to Answer Questions
format your data using spreadsheets and SQL. formulas, functions, and SQL queries as you conduct your analysis. how to aggregate data in spreadsheets and by using SQL; combine data from multiple sources; temporary tables; create new tables; Data validation; data calculations. make predictions; make data-driven decisions; what story is the data telling? will the data solve a problem?
6. Share Data Through the Art of Visualization
data visualizations, such as visual dashboards, can help bring your data to life. Tableau, a data visualization platform. compelling narrative through data stories. principles and practices involved with effective presentations. best practices to a Q&A with your audience.
7. Data Analysis with R Programming
programming language known as R. You’ll find out how to use RStudio. R packages. how R lets you clean, organize, analyze, visualize, and report data; identify patterns, sort, filter. Tidyverse package. R DataFrames. R Markdown for documenting R programming.
8. Google Data Analytics Capstone: Complete a Case Study
complete an optional case study. you’ll choose an analytics-based scenario. Learn the benefits and uses of case studies and portfolios in the job search. job interview scenarios and common interview questions. Discover how case studies can be a part of the job interview process. complete your own case study for your portfolio.
Tecnical Skills & Course Numbers
Spreadsheets (1,3,4 5) SQL (1,4) Big Query (3,5) Kaggle (3) Tableau (6) R (7)
Getting A Job
Can you get a job with this certificate? Here’s a YouTube video by Luke called They became Data Analysts with THIS – Google Data Analytics Certificate: One Year Later.
You work at your own pace. There is no penalty for late assignments; to earn your certificate, all you have to do is complete all of the work. If you prefer, you can extend your deadlines by returning to Overview in the navigation panel and clicking Switch Sessions.
Here’s a review of the course called Google Data Analytics Professional Certificate Review by Aliena Cai. The course starts off easy and intuitive, then get way more technical.
One criticism of the course is that it teaches R instead of Python. Apparently, R is great for quick and dirty data exploration, analytics and statistics, but Python is really required for some deeper data science exploration.
What’s Missing?
The course was great, but there is more. Here is a video on that topic on YouTube called What Data Science Courses DON’T TEACH YOU by Thu Vu. The first one in the video is Good Coding Practices. The next one is Data Visualization Design. The third one is Statisitical Analysis Techniques. The forth one is how to use Git Version Control. The fifth one is Code Performance. This video was done before the next Google course came out called Google Advanced Data Analytics.